Methods Of Offering Guidance On Common Language Usage

ABSTRACT

Usages of language are analyzed in ways that are at least partially language independent. In preferred embodiments, portions of a document are hashed, and the resulting hash values are compared with each other and with those of other documents in real-time. Analyses can be used to gauge conformity of a document to one or more standards, to provide suggestions to the author, and to filter email.

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 11/745,306 filed May 7, 2007 and U.S. provisionalapplication Ser. No. 60/798,956 filed May, 8, 2006. These and all otherextraneous materials discussed herein are incorporated by reference intheir entirety. Where a definition or use of a term in an incorporatedreference is inconsistent or contrary to the definition of that termprovided herein, the definition of that term provided herein applies andthe definition of that term in the reference does not apply.

FIELD OF THE INVENTION

The field of the invention is linguistics and speech processing.

BACKGROUND OF THE INVENTION

Most usable word processing applications incorporate some form ofautomatic spelling or grammar checking systems to aid an individual whenediting a document. For example, Microsoft® Word® indicates that wordsare misspelled by underlining the word in a red line or indicates that aphrase is grammatically incorrect by underlining the phrase in a greenline. The individual clicks on the word to gain insight on alternativeapproaches to spelling or grammar. By selecting one of the alternatives,the individual can edit the document to improve the document'sreadability.

Unfortunately, spelling or grammar checking systems are lacking incapability, especially when migrating the checking system from onelanguage to another. For example, an English document checker iscompletely useless for Japanese due to the differences in grammar,alphabets, or character representation. Furthermore, many spelling orgrammar checking systems do not find subtle errors. Nor do they findwords having uncommon spelling or uncommon phrasing practices that wouldbe considered outside common usage.

Consider the following properly formed sentence: “The engineer walkedinto the lab.” Someone who works in an engineering group mightaccidentally write the sentence as follows: “The engineering walked intothe lab.” Notice the accidental “ing” added on the end of the word“engineer.” Microsoft Word's spelling or grammar checker does not catchthis problem (at the time of writing this document) because the word“engineering” could be a noun and; therefore, could be the subject ofthe sentence. Although the sentence could be as intended, it is unlikelyto be correct because the construction of the phrase is particularlyunusual with respect to common usage.

As used in this application, the term “common language” refers not tosimilarities of one language with another, but to similarities in usageof languages. With that distinction in mind, the current inventors haveappreciated that methods are needed that can identify similarities inusages in any language, i.e. in an language independent fashion.

Previously, methods include the use of rule-based systems that attemptto incorporate knowledge of semantics, syntax, or extensive databasescomprising correct forms of words. The following patent applications,for example, reference using natural language rules to aid documentusers in editing documents:

20060004563; 20050273336; 20050273318; 20040059730; 20040059718;20040059564; 20030097252; 20030069877; 20030061201; 20030061200;20030004716; 20030033288

Similarly, the following issued patents reference using natural languagerules to aid individuals editing documents:

U.S. Pat. Nos. 6,928,425; 6,820,075; 6,778,979; 6,658,627; 5,995,920;5,666,442; 4,914,590

While these references address their respective problems adequately,they do not fully cover the capabilities desired by individuals editingdocuments. Natural language processing has been around for many yearsand focuses on employing the “rules” of the natural language so asoftware program can help the individual identify potential problemswithin their documents.

U. S. Patent application number 20030033288 and its corresponding U. S.Pat. No. 6,820,075 offer auto complete capabilities to users based onsurrounding text within the document. Contextual information surroundinga document fragment forms the basis for a query into a database. Thedatabase returns candidates for completing the fragment or forcorrecting errors. However, these references and the others listed abovedo not teach how to provide guidance on the usage of a common languagein a language independent manner through a statistical approach.

A publication accepted at the 2006 Society for Industrial and AppliedMathematics (SIAM) Conference on Data Mining on Apr. 20 to 22, 2006,titled “Using Compression to Identify Classes of Inauthentic Texts”authored by M. Dalkilic, W. Clark, J. Costello, and P. Radivojac teachesa method for using compression algorithms to indicate if documents havecharacteristics of authentic documents written by humans. Although thepaper offers several insights into statistic document analysis, thepaper does not teach, suggest, or motive using a guidance filter tooffer insight into creating a document that conforms to a commonlanguage.

Thus, there remains a considerable need for methods or apparatus thatguide an individual on the usage of a common language.

SUMMARY OF THE INVENTION

In the present invention, systems and methods provide guidance on theusage of language within a document. Contemplated methods includecreating a guidance filter associated with the common language in waysthat are at least partially language independent. Such a guidance filtercould, for example, include a database comprising tokens relating toportions of the document. The tokens can include hash values associatedwith the portion of the document or neighboring portions.

Analyzed portions can be rather short, such as a word, a phrase, or asentence, or can encompass an entire document or group of documents.

In preferred embodiments the document or portion passes through theguidance filter; possibly through a software program on a computer, inreal-time as the document is being edited or after the document has beenedited. A user of the document can thereby gain insight into how wellthe document conforms to the common or accepted usage by receiving anindication of conformity. In some embodiments, the user sees ahighlighted word indicating that word might not conform to the usage. Inother embodiments, the user is offered a drop-down list that indicatesalternative portions. Further embodiments include email filters thatinclude guidance filters to determine the probability an email comesfrom a source or to determine the probability an email is undesirable.

Alternative embodiment includes a computer readable memory thatcomprises a set of instructions for execution on a computer to offer awell-formedness guidance filter. The filter includes a databaseassociated with a common language and has a statistical metricrepresenting the usage of a portion of a document within the commonlanguage.

More generically, an additional embodiment includes a databasecomprising statistical information relating to how often a tokenrepresenting a piece of data appears in a dataset. The token representsa sequence of information from set of data. For example, a set of dataincludes documents representing a Microsoft Word document, stock priceinformation, music, gene sequences, or other data represented in afashion that can be tokenized.

A Word document simply represents a string of words, white space,punctuation, formatting codes, or other information. Stock prices can beconsidered a series of dollar amounts over time. Music represents asequence of notes, tones, chords, measures, or other musical parameters.Therefore, one can consider the inventive subject matter to includeapplications involving documents associated with stock prices, email,music, or other data that can be tokenized.

Glossary

The following descriptions refer to terms used within this document. Theterms are provided to enhance clarity when discussing the variousaspects of the invention matter without implied limitations.

The term “common language” means any human understandable structuredcommunication means from which statistics can be derived. For example,common languages include English, Japanese, Spanish, or other spokenlanguages. Within the context of this document, common language alsoincludes sign language, Braille, or even computer languages used forprogramming. Common languages also include natural languages becausethere are many documents from which statistics can be generated thatemploy natural languages throughout the world. It is contemplated, thatthe inventive subject matter could be extended beyond common languageusage into other markets including video editing, audio editing, orediting other forms of documents. A common language falls within thegroup of data sets having a linear structure. Other linear structuresinclude music, stock price data, or other data sets that have elementsfollowing each other. It is also contemplated that one could apply thedisclosed techniques to more than one dimension, a photograph forexample.

The term “guidance filter” means a filtering mechanism based on theusage of the common language. In preferred embodiments, the guidancefilter comprises a statistical accumulation of information rather than ajust a rule based accumulation of information about the common media. Itis contemplated the guidance filter can be used in conjunction with arules bases checker to enhance the offering of both systems.

The term “token” means an abstraction that represents a piece of data.Tokens can be the piece of data itself; however, more commonly a tokenis simply a short hand for representing the data which can be used toaccess statistical information in a database. For example, a hash of asequence of data could represent a token. Tokens can be represented by afunction that has the piece of data as an input and the token is theoutput of the function. Tokens can also comprise other tokens. Tokenswill be described more fully in a later section of the document.

The teachings herein may be advantageously employed by developers ofdocument editing or viewing packages. Guidance filters may be employedby document editors to gain insight into how well their documentsconform to a usage of a common language.

Various objects, features, aspects, and advantages of the presentinvention will become more apparent from the following detaileddescription of the preferred embodiments of the invention, along withthe accompanying drawings in which like numerals represent likecomponents.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 represents an example of how prior art document checkers providean indication that there is a potential problem with a portion of adocument.

FIG. 2 presents an example embodiment of a guidance filter that usestokens as an index into a database.

FIG. 3 illustrates an example embodiment of offering an individual anindication of how well a document conforms to the common language.

Various objects, features, aspects and advantages of the presentinvention will become more apparent from the following detaileddescription of preferred embodiments of the invention, along with theaccompanying drawings in which like numerals represent like components.

DETAILED DESCRIPTION

The following detailed description of the inventive subject matter usesseveral example embodiments to convey concepts presented in anunderstandable manner. However, the scope of the inventive subjectmatter is not limited to the examples, but rather should be interpretedto the broadest extent. For example, English is used as the commonlanguage example; however, the concepts presented can be appliedgenerally to other common languages.

Overview

FIG. 1 represents an example of how prior art document checkers providean indication of a potential problem with a portion of a document.Document 100 comprises four sentences that include well formed sentence110, incorrect grammar sentence 120, incorrect spelling sentence 130,and not well formed sentence 140. These sentences were placed in aMicrosoft Word document with active document checking Sentence 110 iswell formed and does not trigger the document checker. However, sentence120 does trigger the document checker because of subject-verb agreementas indicated by portion 125 having the noun-verb phrase “cat jump.” Inaddition sentence 130 also triggers the document checker because portion135 is misspelled, “jmp” as opposed to “jump.” Sentence 140 does nottrigger the document filter even though it is not well formed. Thedocument checker should find a problem with portion 145, the word“jumping” because sentence 140 is not well formed. The document checkerdoes not trigger because sentence 140 passes through all of its rules.One would find it desirable to have a document checking system that alsoidentifies problems associated with well-formedness that can bridgelanguages.

Guidance Filter: Introduction

A guidance filter, in a preferred embodiment, comprises software thatuses statistics compiled over a set of documents to provide a userinsight into how their document conforms to the statistics. For example,the problems associated with sentence 140 can be identified a through aguidance filter. The guidance filter compares portion 145 against howother documents use portion 145 in similar circumstances. For example,portion 145, the word “jumping,” is related to the word “cat” and thewords “the,” “lazy,” or “dog” in sentence 140. Word pairs (“cat”,“jumping”), (“jumping”, “the”), (“jumping”, “lazy”), (“jumping”, “dog”)can be formed and checked against the usage of similar pairs in otherdocuments. If the guidance filter indicates that the pair (“cat”,“jumping”) is found to have low occurrence, the guidance filter mightfind that the pair (“cat”, “jumped”) or (“cat”, “jumps”) have a higherfrequency occurrence. Consequently, an indication can be provided to anindividual writing sentence 140 of a potential better constructionassociated with portion 145. Therefore, the guidance filter offers andindication of the well-formedness of the document. Using word pairs isuseful; however, one should appreciate that raw words are languagedependent; consequently, a more useful approach employs an abstractionof the words in a manner that is language independent. Obviously thisexample is extremely simplified and those skilled in the art of documentcheckers will recognize there are more sophisticated variationspossible, all within the scope of the concepts presented.

Guidance Filter: Tokens

A preferred embodiment of the guidance filter comprises a database oftokens. Tokens represent an abstraction of a piece of data. In otherwords, portions of a document can be represented by a token rather thanby the portion of the document itself. For example, the token of theword “cat” could be any variable, value, structure, symbol, hash value,or other function of the word “cat.” Ideally the token of the portion ofthe document should have some level of uniqueness associated with it.For example, the token of the word “cat” could be any of the following:

-   -   [WORD1]: Simply a sequence of symbols representing the first        word in a database that could be the word “cat.”    -   cat: The string of characters representing the word itself        encoded in ASCII, UNICODE, or other encoding schemes.    -   MD5(“cat”): An MD5 hash function of the word “cat.”

Each of these example “codes” can be used to represent the word “cat” ina manner that is essentially unique. For the purposes of this document,a token of one or more objects is represented by the following:

TOKEN(object1, . . . , objectN)

where each object could be a string, another token, a number, or otheritem that has utility within the guidance filter. Example objectsinclude tokens that represent white space in a document, punctuation,rule information, part of speech, relative position within a document,or other information relating to the common language. All objects arecontemplated because the purpose of the tokens is to encode statisticalinformation in a language independent manner for the common language;consequently, the objects encode the desirable characteristics of theguidance filter. This implies that the creator of the guidance filtercan create a wealth of information about the statistics of the commonlanguage that can be data mined or brought to bear when comparing otherdocuments to the common language.

In a preferred embodiment, the tokens are associated with the portionsof a document where the portion can comprise a character, word, groupsof words, rules, phrase or phrases, sentence, paragraph, or othergrouping of information. It is contemplated that the concepts presentedherein could be extended to other related document portions includingcompilations of many documents, document metadata, white space usage,formatting codes, or others. A token can be either direct or indirect. Adirect token has a value that comprises at least part of the portionfrom the document. For example, referring back to FIG. 1, portion 135could have a direct token of:

TOKEN(“jmp”)==“jmp”

where the token of the string of characters “jmp” is the string ofcharacters itself. Or, in the case of portion 125, one or more tokenscould be generated associated with the word “jump” as follows:

TOKEN(“cat”, “jump”)=={“cat”, “jump”}

TOKEN(“jump”, “the”)=={“jump”, “the”}

TOKEN(“jump”, “dog”)=={“jump”, “dog”}

In these example tokens associated with portion 125, the word “jump,”are simply represented by a set of character strings themselves.Furthermore, the use of word stemming can be used to create directtokens where, for example, tokens TOKEN(“jump”) is similar to theTOKEN(“jumping”) because they have the same stem “jump.” These examplesshow that a single token can be used to represent multiple portions of adocument. One should note that a string including “cat” or “jump” arejust values binary encoded in a computer memory. As far as a database ofstatistics are concerned, there are no language dependencies included inthe encoded strings. This means the software used to form the databaseof tokens for English would work as well on another language becausesoftware forming tokens operates on the encoding in memory.

Indirect tokens are tokens that have a value derived from the portion.An example of an indirect token includes calculating a hash value fromthe portion. Examples of indirect tokens of portion 125 could include:

TOKEN(“jump”)==HASH(“jump”)

TOKEN(“jump”, “the”)==HASH(“jump”, “the”)

TOKEN(“jump”, “dog”)==HASH(“jump”, “dog”)

where each token has a numerical value based on a hash function.Preferred hashing functions include MD5, or SHA-1; however, all otherhashes that provide statistical uniqueness are also contemplated. Eventhough MD5 is a broken hash, it is still acceptable because the hashvalue is not necessarily used for security reasons. The resulting numberof bits from the hash is preferably at least 32-bits; however, othersized hashes are contemplated. It is also contemplated that directtokens and indirect tokens can combine to form other tokens that offervalue within the guidance filter.

Other types of indirect tokens are also contemplated including thosethat operate as a function of the portion other than hashes. Especiallypreferred functions include those that provide a “nearness” measure. Forexample, it is useful to have the two tokens TOKEN(“jump”) andTOKEN(“jumping”) close to each other to aid in searching the database oftokens. Compression functions could offer this ability including LZWcompression. Compression techniques often use tokens to representslarger groups of data. Consequently a token for “jump” might be used torepresent “jumping.” For example:

TOKEN(“jumping”)=={TOKEN(“jump”), TOKEN(“ing”)}

where the token value for “jumping” is close in value to the value for“jump.” The continue the example, if the value of TOKEN(“jump') is “A”and the value of TOKEN(“ing”) is “B,” then TOKEN(“jumping”) could have avalue of “AB” which, from a searching standpoint, is close to “A.”

In the preceding example word pairs were used to create hashes. Oneskilled in the art of natural language processing will also understandother types of information can be combined to form portions of thedocument. Examples of other information include document metadata,punctuation, parts of speech, or phrase types. Document metadatarepresents data within the document relating to the document contents.An example use of parts of speech would be creating a hash based on aword and the part of speech of the following word or preceding word. Forexample, a token in the database could be represented by HASH(“cat”,[VERB]) where [VERB] is a key used within the hash to represent that thefollowing word is a verb. It is also contemplated that the token couldencode verb tense as well. In some sense, the token encodes the rules ofthe common language. Rules include the syntax, semantics, or otherstructures. All variations of generating tokens are contemplatedincluding those that are tokens of tokens. In some embodiments thefollowing example tokens are useful when analyzing a document:

-   -   [PUNCT]: Represents any of a series of punctuation including        commas, periods, quotes, or others. In other words, the token of        any punctuation mark results in a value of    -   [PUNCT], for example TOKEN(“,”)==[PUNCT]. If a word is next to        the punctuation mark as in this sentence, a token can be        generated by TOKEN(“example”, [PUNCT]).    -   [SENT-BEGIN]: Represents that a word begins a sentence. For        example, in this sentence a token can be generated by        TOKEN(“For”,[SENT-BEGIN]).

Again, one should note the tokens [PUNCT] and [SENT-BEGIN] are simplyvariables representing a concept and could be any value. Clearly, thereare nearly a limitless number of possible tokens that would be useful.This implies guidance filters employing the tokens can have variousutility to a user depending on how the tokens are created.

One should also appreciate that tokens could also be generated for anysequence of data. It is also contemplated that tokens can be generatedfor stock price history, used to identify email spam, used to profilepreferred musical notes sequence, or used for other data sets.

Guidance Filter: Database

FIG. 2 presents an example embodiment of a guidance filter that usestokens as an index into a database. In this constructed example, thedatabase comprises token index 210 into a frequency 220 with which thattoken is expected to occur in a common language. Entry 221 through entry229 in database 200 indicate what the frequency of occurrence is withinthe common language for the hashed tokens. Entry 221 indicates that theword pair (“cat”, “jmp”) does not appear within the common languagebecause the frequency of appearance is zero where other entries havehigher frequency of occurrence. It is contemplated that a zero-frequencytoken might not be in the database; consequently, a query for the tokencould result in a NULL return set. Furthermore, the word pair (“cat”,“jumping”) at entry 225 has a relatively low frequency of occurrence;however, could still be considered viable. Database 200 can also beindexed by the portions as well as indicated by fields portion 230through portion 240. The second method of indexing provides for theability to find alternatives to a low frequency entry. As an example,using an index of “cat” from portion 225 would return entries 222, 223,or 224. Entry 221 is not returned because such a token would have a NULLreturn value because no statistics were accumulated for that token. Thenthe guidance filter can supply the most likely candidate for correctusage from the return set.

The reader should note the method of gaining access to acceptablealternatives can vary. In the preferred embodiment, a single informationsystem comprising the guidance filter database also includes thealternatives. It is also contemplated that the guidance filter can beused to enhance existing document checkers or document checkers yet tobe invented. Other document checking systems are expected to offeradditional methods for supplying likely alternatives to the individualediting the document. A closer inspection of document analysis isprovided later in this document.

When creating the database, some embodiments find it useful to preparedata sets for entry into the database. Preparation of the data setsincludes scrubbing documents to ensure consistency of representation.This ensures that all tokens are essentially “neutral” with respect toeach other from one document data set to another. Example scrubbingincludes removing white spaces and replacing them with a tokenrepresenting the type of white space, converting all upper case lettersto low case letters, or other similar preparation. The preparation ofthe data sets depends on the type of guidance filter that is going to becreated and the statistics that would be useful.

Guidance Filter: Statistics

Frequency 220 represents a preferred statistic or metric associated withthe tokens used as part of the guidance filter; however, otherstatistics can also be employed. Examples of other statistics associatedwith the tokens include density, rate, variation, or others. In fact,all manner of statistics are contemplated. Density represents the numberof times a portion appears within a give sub-section of a document. Raterepresents the number of times a portion appears within a given unit oftime. Variation represents the pattern of usage of the portion withinthe common language.

In the preferred embodiment, statistics of usage are built by analyzingmany examples of the common language. For example, if an individualwished to create a guidance filter based on the works of WilliamShakespeare, the statistics could be used by running the collected worksof Shakespeare through a program that collects the statistics. As theprogram analyzes a document, it could calculate the hash value for eachportion. In one embodiment, the program calculates a triplet of hasheswhere the portion of the document represents a word or a word pair. Thetriplet includes a hash for a word, a hash for the word and itspreceding word, or a hash for the word and its following word. Then, foreach hash value the program increments the number of occurrences forthat hash. Therefore, the database can be considered to represent ahistogram of the hashes. It is contemplated that the statistics arecompiled over a sufficient data set of the common language that thestatistical value associated with a token comprises a value of at least100. More preferably, the value would exceed 1000. The more datarepresenting the common language used to generate the statistic thebetter. Increasing the statistics associated with each token providesgreater information when comparing documents to the common language.

Guidance Filter: Language Independence

Because the database of the guidance filter embodies generic tokens andstatistics, the reader should appreciate that the database can be builtfor any language because a token abstractly relates to any languagestructure. For example, a token can be created based on two kanjicharacters next to each other just as easily as two words. In thisrespect, the guidance filter can be constructed in a manner that islanguage independent.

Guidance Filter: Thresholds

The use of the guidance filter, in one embodiment, is governed by athreshold setting associated with the statistics. The threshold settingrepresents the point at which an indication should be offered to theindividual using the guidance filter. When the individual types in aportion of the document, the guidance filter will be consulted. If thestatistics of the filter's database indicates the usage of the portionin the common language falls above (or below) the threshold, then theindividual is presented with a message.

A preferred embodiment of a guidance filter comprises software and adatabase. The software represents a program of instructions that executeon a computer running an operating system. In some embodiments, thesoftware is packaged as an off the shelf product; however, it could alsobe included within other packages. The database can be pre-builtautomatically or built as necessary by an individual associated with thedocument. It is contemplated one could employ many different classes ofdatabases for different reasons. An individual could employ aShakespeare database to write documents that conform to Shakespeare'swriting style, or the individual could employ a medical database towrite documents for medical journals.

Thresholds also provide for a self reinforcing database. In oneembodiment a community is formed where individuals submit their words toan on-line system. The system only incorporates new documents if the newdocuments fall within the threshold of the existing database. Thedatabase would initially be seeded by documents that conform to thedesired common language.

Guidance Filter: Evolution

A preferred guidance filter comprises statistics relating to tokens.This implies that the database of statistics can be changed dynamicallyas desired or as necessary. For example, additional data can be added tothe database to increase the coverage of the common language usage. Asmore data is added, the more useful the guidance filter becomes. Oneshould also note that data can be subtracted. One could effectively“remove” a document from the common language by calculating all relevanttokens and subtracting their statistics from the database. If theguidance filter represents all the works of Shakespeare and one of thedocuments proves to be written by his wife, then the document can beremoved by subtracting its statistics.

It is contemplated that a guidance filter could be created for varioustime periods. In some embodiments, multiple guidance filters are createdfor each time period of interest. One could create a guidance filter foreach decade of the 20^(th) Century, and then use the guidance filter toaid in dating other documents. In this sense, using the guidance filteras a forensic tool falls within the scope of the inventive material.

Rules based document checkers are rigid. Document checkers that follow astatistical approach are more dynamic, flexible, or adaptive. Employingthe enclosed techniques allows for an automatic update of the guidancefilter because new statistics will cause the database to change ratherthan having an individual encode rules.

Document Analysis

Once a guidance filter exists, a document and its portions can beanalyzed to find those portions that require attention. Simple analysistechniques were previously presented; however, a more detailed review isin order.

One should recognize the data stored in the guidance filter is a wealthof information relating to the common language that can be brought tobear against a document. In some embodiments, the guidance filterconverts or stores normalized values of the statistics in a manner thatindicates the probability of a token occurring in the common language.The guidance filter can capitalize on these probabilities to increasethe accuracy of the guidance.

As an example consider the tokens TOKEN(“cat”, “jumping”) andTOKEN(“jumping”, “dog”) from document 100. In this example the article“the” and adjective “lazy”, are ignored. Database 200 indicates thatboth these tokens occur fairly frequently and represent potentiallyviable text. Neither of these tokens by themselves offers a strongindication of where the problem is relative to an appropriate thresholdvalue. However, when the probabilities of the tokens are multipliedtogether, their combined probability is much smaller which could resultin triggering the threshold function. One should keep in mind that thedatabase does not have knowledge of the current document, but theguidance filter does. Consequently, the guidance filter can compare thetwo tokens to discover that the problem in the document is likely to bewith the word “jumping” rather than “cat.” Effectively, the guidancefilter searches the surrounding area of the portion of interest to findcorrelations that deviate from the threshold function.

The guidance filter, preferably, includes the ability to query thedatabase in many ways. For example, when searching for alternatives, theguidance filter could employ regular expressions, word stemming, wildcards, or other mechanisms to find potential suggestions. Referring backto the example problem with the word “jumping,” the guidance filtercould search the database, or other information system, for those tokensof the form TOKEN(“cat”, *) and TOKEN(*, “dog”). Two results sets arereturned for each query. The intersection of the result set indicateslikely candidates for alternative text. Furthermore, the alternativescan be ordered by the probability of occurring in the common language.Additionally, the guidance filter could query the database using variouspermutations of portions to see if it can find a best fit for the set ofportions currently being reviewed. This approach is beneficial whenwords are transposed and need to be flagged, for example, when theguidance filter encounters a split infinitive.

Document Analysis: Examining Permutations

One skilled in the art of document checkers will appreciate that usingtokens and statistics offers greater ability at document analysis. Inpurely rules based systems, the rules more often than not haveexceptions, which themselves have exceptions. However, through usingstatistics of the common language, a well-formed document can becreated. For example, if the guidance filter encounters a problem area,the guidance filter could try querying the database of tokens throughdifferent permutations of words in the problem area to find thepermutations that has the best conformance to the common language. Thisapproach is beneficial when words are transposed, but still pass throughrules based systems.

Indication of Common Language Usage

FIG. 3 illustrates an example embodiment of offering an individual anindication of how well a document conforms to the common language.Document 300 in this example is identical to document 100. Sentence 310is well formed, sentence 320 has incorrect grammar as shown byindication 325, and sentence 330 has incorrect spelling as shown byindication 335. Finally, sentence 340 is not well formed with respect tothe common language. Document 300 is passed through a common languageguidance filter and the filter senses that the words “cat jumping” doesnot pass a threshold associated with the common language. Consequently,the individual writing the document is offered a signal via indication345. In a preferred embodiment, the individual can click on theindication as they would with Microsoft Word indictors to displaydrop-down list 355. Drop-down list 355 offers the individual alternativestructures that could be used in place of the portion of the documentthat did not pass the threshold. Preferably the list is ordered as afunction of the statistical value of the alternatives. For example, thealternative that has the highest conformance to the common language isplaced first in the list. It is contemplated that the user could ignorethe suggestion or could select a suggestion. It is also contemplatedthat indication 345 could couple to the threshold settings of theguidance filter in a manner that the individual can see at a glance howfar the portion deviates from the common language. For example, ifindication 345 is red then the deviation could be far from the commonlanguage or if indication 345 is blue the deviation could be small.

Although the example in FIG. 3 shows drop-down list 355 and indication345, all other forms of providing an indication of how closely theportion of the document conforms to the usage of the common language arecontemplated. For example, the entire document could have one or morevalues where each value represents a metric that indicates conformity tothe common language. Values could include a standard deviation measureor a percent value. Additionally, individual portions of the documentcould have their color changed based on passing the portion of thedocument through the guidance filter.

The indication could be offered at any time during the document's lifetime. Preferably, the indication is offered during real-time as thedocument is being edited in a similar manner as traditional documentcheckers operate. Alternatively, the document could be post processed.For example, if the document is in a final form, Adobe® PDF format forinstance, a document viewer could show where the document lacksconformity to the common language.

Example Uses

The following examples are presented to offer the reader an idea of howthe inventive subject matter could be deployed advantageously to helpindividuals create or use documents. In no way do the following examplesplace any limitations of the inventive subject matter.

In one embodiment, an individual uses a guidance filter on-line througha web browser. The user creates a document local to their computer, oron-line, and submits to the document to a service that checks thedocument for conformity to the common language. Examples servicesinclude translation services from one language to another, technicalreview of papers, or possibly a site that authenticates the document bycreating a metric associated with the document. It is contemplated thatan author could submit their writings to a site which builds a databasethat representing the writer's works. The writer then has a method foridentifying his writing style. It is expected that companies such asGoogle™ who recently purchased WRITELY.COM would find this technology auseful, beneficial feature for their customers.

Similarly, in another embodiment, an individual wishes to securelytransmit information relating to their document. In this case, ratherthan submitting the document, the individual could submit only thetokens associated with the database. Preferably the tokens would be astrong hash function, possibly 128-bits in length, to providestatistical uniqueness over the myriad of possible tokens that aguidance filter could employ. An astute reader will realize that thetokens are independent values which imply they can be sent to theguidance filter out of sequence thereby enhancing the security of theexchange. In other words, one token is not connected to another token.In addition, when exchanging tokens over a network, “false tokens” couldbe sent to a guidance filter to further mask the exchange and hidedocument data.

In yet another embodiment, a guidance filter is combined with existingspell checkers or grammar checkers to enhance their capabilities. It iscontemplated that companies such as Microsoft would be interested incombining the disclosed matter to help individuals improve theirwritings. For example, those who suffer from dyslexia often transposewords or letters in a manner that traditional document checkers wouldmiss. However, through the use of the statistical guidance filter, thedyslexic would find their problem areas. By combining existing documentcheckers with a guidance filter, the number of false errors could alsobe reduced.

A possible type of a document checker includes an email spam filterwhere the spam filter can determine if an email is undesirable. The spamfilter including a guidance filter has several advantages. One advantageincludes the ability to filter undesired email capable of making itthrough existing filters. For example, spammers include random,nonsensical words to bypass existing spam filters; however, the guidancefilter offers the spam filter an indication that the spam is above orbelow a probability threshold for acceptance because the nonsensicalworks do not conform to a common usage. A second advantage includesdetermining how well an email corresponds to an author or other sourceof the email. The guidance filter can build a model, or a database, of acommon language usage of the source by submitting emails from theindicated source for analysis. Then, the guidance filter develops themodel for the email source and the guidance filter can provide the spamchecker a probability that the email conforms to that source's commonlanguage usage. In this manner, a user can gain confidence that an emailcame from the source as indicated.

Yet another embodiment includes combining the guidance filter with voicerecognition software or optical character recognition (OCR) both ofwhich represents non-written sources for a document. Voice recognitionsoftware has difficulty in distinguishing between homonyms and knowingwhat the correct word form to use in a document. It is also contemplatedthat a physical recognition system could be used as the source of adocument, for Braille documents for example. By running a documentthrough the guidance filter, the recognition system can offer a higherdegree of recognition.

Advantages

Using a guidance filter to aid individuals in ensuring their documentsconform to a common language usage has several advantages. One advantageincludes offering those who suffer language problems a method foridentifying problem areas. As mentioned previously, dyslexics couldvisually see where their documents deviate from common usage.

Employing guidance filters as described opens new markets for documentediting products. Guidance filters or databases can be created forvarious classes of documents. It is contemplated that databases could becreated that offer individuals guidance on how to make their documentsconform to the style of other authors. In some markets, in legalindustry for example, documents must have strict structures. Individualscould submit their documents to a paid service for review or individualcould purchase specialized databases.

Using hashed tokens also improves performance of communication. Asindicated above, tokens can be exchanged over a network to engage aremote guidance filter. When hashed tokens have a small size (smallnumber of bits) relative to the corresponding portion of the document,then the amount of data exchanged over the network is reduced.

Additional Considerations

The inventive subject matter has many possible applications beyondoffering guidance on document editing. The following concepts representpossible alternative uses for tokenized information stored in a databaseof statistics:

-   -   Stock trend analysis where a database of tokenized data points        from well performing stocks is built, then other stocks could be        compared to see if they have the characteristics of a well        performing stock    -   Identifying music that conforms to a listener's preferred data        set. A radio could build the database as a listener changes        stations, then the radio could suggest which stations are        playing music or other audio streams that conform to the user's        preferences.    -   As mentioned previously guidance filters can be used to within        spam filters. After delivery, a guidance filter could find those        messages that conform to spam and mark them or delete them.    -   Publishers could create a database of works that their customers        like to ensure new works fit the model. For example, new romance        novels could be compared to those novels that generated the        highest revenue in the past to ensure that the new novels have        the same characteristics as the old novels.    -   Statistical methods could be employed to automatically generate        alternative words lists. Word list include synonyms, thesaurus,        or other lists where words have a common relationship with        respect to each other.    -   Office document style guides could be created to ensure office        documents conform to proper form.    -   Gene sequences could be tokenized to see if they match known        structures. The source of the gene sequence document could be        generated by a machine analyzing the gene sequences.    -   Dialects of the same language can be distinguished

The above alternative concepts can be generalized to some degree. It iscontemplated that the disclosed system would be useful for other streamsof information where the stream can be tokenized and analysis can beconducted on the statistics of the tokens.

Hardware

Other aspects relate to hardware associated with the inventive subjectmatter. It is contemplated that one could develop hardware for storing,prototyping, manufacturing, manipulating, managing, packaging, testing,physically controlling or supporting, or for other activities associatedwith the physical aspects of the inventive subject matter. Therefore,the inventive subject matter includes systems, methods, or apparatus fordeveloping, producing, manufacturing, or running the hardware. In thissense, the hardware falls within the scope of the inventive subjectmatter.

Software

In still another aspect, it is contemplated that one could writesoftware that would configure, simulate, or manage various aspects ofthe inventive subject matter and their associated infrastructure. Fromthat perspective the inventive subject matter includes methods ofwriting such software, recording the software on a machine readableform, licensing, selling, distributing, installing, or operating suchsoftware on suitable hardware. Moreover, the software per se is deemedto fall within the scope of the inventive subject matter.

Thus, specific compositions and methods of offering guidance on a usageof a common language have been disclosed. It should be apparent,however, to those skilled in the art that many more modificationsbesides those already described are possible without departing from theinventive concepts herein. The inventive subject matter, therefore, isnot to be restricted except in the spirit of the disclosure. Moreover,in interpreting the disclosure all terms should be interpreted in thebroadest possible manner consistent with the context. In particular theterms “comprises” and “comprising” should be interpreted as referring tothe elements, components, or steps in a non-exclusive manner, indicatingthat the referenced elements, components, or steps can be present, orutilized, or combined with other elements, components, or steps that arenot expressly referenced.

What is claimed is:
 1. A computing system comprising: a non-transitorycomputer readable memory storing software program instructionsrepresenting a guidance filter; and a computer coupled with the memoryand, upon execution of the instructions, is configured to: analyze apassage of a document being edited by a user to find a portion thatrequires attention use the guidance filter to search a surrounding areaof the portion to find correlations among document tokens that deviatefrom a threshold function associated with a common language signal viaan indicator in the document that at least one of the document tokensdeviates from the common language based on the correlations; and allowthe user to select an alternative structure from a list where thealternative structure can be used in place of the at least one documentportion and conform to the common language.
 2. The system of claim 1,wherein the guidance filter comprises a statistical database of tokensrelated to the common language.
 3. The system of claim 2, wherein thedatabase comprises a frequency with which each of the tokens appears ina common language dataset.
 4. The system of claim 2, wherein the tokenscomprise hashes of words from the common language dataset.
 5. The systemof claim 1, wherein the threshold function is configured to determinelikelihood that the passage is written as intended by the user.
 6. Thesystem of claim 1, wherein the document tokens comprise a hash for aword in the portion and it's preceding word, and a hash for the word,and a hash for the word and its following word.
 7. The system of claim 1wherein the list comprises at least two alternative text candidates. 8.The system of claim 7, wherein the at least two alternative textcandidates are order according to a probability of occurring in thecommon language.
 9. The system of claim 1, wherein the indictorcomprises an underline indicator showing deviation from the commonlanguage.
 10. The system of claim 9, wherein the underline indicatorshowing deviation from the common language is blue.
 11. The system ofclaim 1, wherein the document comprises a word processing document. 12.The system of claim 1, wherein the document comprises at least one ofthe following: a web-based document, a local computer document, music,an email, and gene sequences.
 13. The system of claim 1, wherein theindicator representing a dyslexic problem.
 14. The system of claim 1,wherein the passage is no longer than a sentence.
 15. The system ofclaim 1, wherein the passage is no longer than a paragraph.
 16. Thesystem of claim 1, wherein the correlations among document tokens takesinto account white space in the passage.
 17. The system of claim 1,wherein the correlations among document tokens takes into accountpunctuation in the passage.
 18. The system of claim 1, wherein theguidance filter is language independent.
 19. The system of claim 1,wherein the memory further stores a document editing program and whereinthe guidance filter is combined with the document editing program.